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Network</h1>
<p><span class="math display">\[
A Multi-Layer Fusion Neural Network for Depression Severity Prediction
--Manuscript Draft
\]</span></p>
<hr />
<p>​ ---计算机视觉，卷积神经网络变种</p>
<h2 id="优点">优点</h2>
<ul>
<li><p>提出了多层融合网络（MLFNET）该网络不需要大规模的预训练过程</p>
<ul>
<li>并且是<strong>端到端</strong>的神经网络</li>
<li>尽管它尚未使用前训练技术</li>
</ul></li>
<li><p>提出了<strong>局部剩余注意（LRA）模块</strong>。根据标准3D残差模块，我们采用跳过连接和注意机制来增加局部信息的传输路径并丰富本地特征表示。</p></li>
<li><p>提出了多层信息融合（MLIF）模块。在深层网络中，在多层网络中的信息传输后，浅层层中的详细信息和边缘信息将丢失。为了保留更多浅层的信息，通过全局跳过连接缩短了浅层和深层之间的信息传输路径。此外，不同层的特征通过特征对齐和注意机制有效地融合，从而保留了低级细节和边缘信息，并包含高级语义信息。</p></li>
</ul>
<h2 id="背景">背景</h2>
<p>抑郁识别的研究可以分为两个一般方向：手工特征方法和深度学习网络。分别从这两个方面看</p>
<ul>
<li>手工特征方法
<ul>
<li>建议使用局部相量化（LPQ）提取面部外观特征，然后使用SVR模型来预测抑郁水平</li>
<li>来自三个正交平面（LGBP-TOP）的本地Gabor二进制图案提取视频中的动态变化信息</li>
<li>Kaya使用Gabor滤波来将图像从空间转换为频域，以获得新的过滤图像集，然后提取由LBQ-TOP方法设置的过滤图像的三个正交平面的动态特征，用于表示该图表示动态面部运动信息</li>
<li>He et
al提出了一个中值鲁棒的局部二进制模式（MRLBP-TOP）操作员，以描述微型和宏中面部的动态变化。</li>
<li>Song等通过动作单元（AU）构建了光谱热图和光谱矢量，该光谱载体用于描述时间序列中的多尺度面部运动特征</li>
</ul></li>
<li>深度学习方法
<ul>
<li>预训练的CNN网络提取面部特征，然后通过多层感知器网络预测抑郁水平</li>
<li>DCNN分别学习了面部外观的静态特征和面部运动的动态特征</li>
</ul></li>
</ul>
<h2 id="研究思路">研究思路</h2>
<figure>
<img
src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/image-20220912135538409.png"
alt="image-20220912135538409" />
<figcaption aria-hidden="true">image-20220912135538409</figcaption>
</figure>
<p>根据3D网络结构提出了一个多层融合网络（MLFNET），该网络结构可以从视频剪辑中提取面部运动功能和上下文相关信息</p>
<p>skip connection structure 降低过拟合风险</p>
<blockquote>
<blockquote>
<p><strong>过拟合，欠拟合 退化三者区别</strong></p>
</blockquote>
<p>欠拟合就是模型没有很好地捕捉到数据特征，不能够很好地拟合数据</p>
<p>拟合就是模型把数据学习的太彻底，以至于把噪声数据的特征也学习到了</p>
<ul>
<li>造成训练误差小，测试误差大</li>
</ul>
<p>退化</p>
<ul>
<li>部分神经元没有效果了，只有少部分神经元在运行</li>
</ul>
</blockquote>
<h3 id="lra-module">LRA Module</h3>
<figure>
<img
src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/image-20220912135851165.png"
alt="image-20220912135851165" />
<figcaption aria-hidden="true">image-20220912135851165</figcaption>
</figure>
<p>该紫色模块具有两个卷积层，每个卷积层的卷积核大小固定为3×3×3。</p>
<ul>
<li>用来增强第一个卷积层的输出特征</li>
</ul>
<blockquote>
<blockquote>
<p>swish激活函数</p>
</blockquote>
<p><span class="math display">\[
f(x)=x⋅sigmoid(βx)
\]</span></p>
<p>Swish函数的特点</p>
<ul>
<li>有助于防止慢速训练期间，梯度逐渐接近0并导致饱和</li>
<li>导数恒大于0。</li>
<li>平滑度在优化和泛化中起了重要作用。</li>
<li><img
src="watermark,type_ZHJvaWRzYW5zZmFsbGJhY2s,shadow_50,text_Q1NETiBAdm9uICBOZXVtYW5u,size_20,color_FFFFFF,t_70,g_se,x_16"
title="fig:" alt="Swish函数的图像" /></li>
</ul>
</blockquote>
<p>与基本的3D残差块相比，LRA模块添加了第一卷积层H（y）的局部增强特</p>
</article><div class="post-copyright"><div class="post-copyright__author"><span class="post-copyright-meta">文章作者: </span><span class="post-copyright-info"><a href="http://yang1he.gitee.io">杨一赫</a></span></div><div class="post-copyright__type"><span class="post-copyright-meta">文章链接: </span><span class="post-copyright-info"><a href="http://yang1he.gitee.io/2022/09/15/A%20Multi-Layer%20Fusion%20Neural%20Network/">http://yang1he.gitee.io/2022/09/15/A%20Multi-Layer%20Fusion%20Neural%20Network/</a></span></div><div class="post-copyright__notice"><span class="post-copyright-meta">版权声明: </span><span class="post-copyright-info">本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank">CC BY-NC-SA 4.0</a> 许可协议。转载请注明来自 <a href="http://yang1he.gitee.io" target="_blank">杨一赫的博客</a>！</span></div></div><div class="tag_share"><div class="post-meta__tag-list"><a class="post-meta__tags" href="/tags/%E5%AD%A6%E6%9C%AF%E6%96%87%E7%AB%A0/">-学术文章</a></div><div 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class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">14</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">7</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">16</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://gitee.com/yang1he"><i class="fab fa-github"></i><span>gitee</span></a></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>公告</span></div><div class="announcement_content">平平无奇的网站</div></div><div class="sticky_layout"><div class="card-widget" id="card-toc"><div class="item-headline"><i class="fas fa-stream"></i><span>目录</span><span class="toc-percentage"></span></div><div class="toc-content is-expand"><ol class="toc"><li class="toc-item toc-level-1"><a class="toc-link" href="#a-multi-layer-fusion-neural-network"><span class="toc-number">1.</span> <span class="toc-text">A Multi-Layer Fusion Neural
Network</span></a><ol class="toc-child"><li class="toc-item toc-level-2"><a class="toc-link" href="#%E4%BC%98%E7%82%B9"><span class="toc-number">1.1.</span> <span class="toc-text">优点</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E8%83%8C%E6%99%AF"><span class="toc-number">1.2.</span> <span class="toc-text">背景</span></a></li><li class="toc-item toc-level-2"><a class="toc-link" href="#%E7%A0%94%E7%A9%B6%E6%80%9D%E8%B7%AF"><span class="toc-number">1.3.</span> <span class="toc-text">研究思路</span></a><ol class="toc-child"><li class="toc-item toc-level-3"><a class="toc-link" href="#lra-module"><span class="toc-number">1.3.1.</span> <span class="toc-text">LRA Module</span></a></li></ol></li></ol></li></ol></div></div><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>最新文章</span></div><div class="aside-list"><div class="aside-list-item"><a class="thumbnail" href="/2023/06/24/An%20Intelligent%20Mobile%20Prediction%20method/" title="An Intelligent Mobile Prediction method with Mini-batch HTIA-based Seq2Seq Networks"><img src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/model_00.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="An Intelligent Mobile Prediction method with Mini-batch HTIA-based Seq2Seq Networks"/></a><div class="content"><a class="title" href="/2023/06/24/An%20Intelligent%20Mobile%20Prediction%20method/" title="An Intelligent Mobile Prediction method with Mini-batch HTIA-based Seq2Seq Networks">An Intelligent Mobile Prediction method with Mini-batch HTIA-based Seq2Seq Networks</a><time datetime="2023-06-23T16:00:00.000Z" title="发表于 2023-06-24 00:00:00">2023-06-24</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2023/03/05/%E5%A6%82%E4%BD%95%E8%8E%B7%E5%8F%96%E4%BD%A0%E4%B8%AA%E4%BA%BA%E8%B4%A6%E5%8F%B7%E7%9A%84openai%E7%9A%84api/" title="无题"><img src="/img/rick1.jpg" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="无题"/></a><div class="content"><a class="title" href="/2023/03/05/%E5%A6%82%E4%BD%95%E8%8E%B7%E5%8F%96%E4%BD%A0%E4%B8%AA%E4%BA%BA%E8%B4%A6%E5%8F%B7%E7%9A%84openai%E7%9A%84api/" title="无题">无题</a><time datetime="2023-03-05T05:57:17.050Z" title="发表于 2023-03-05 13:57:17">2023-03-05</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2023/03/04/%E4%B8%89%E5%88%86%E9%92%9F4%E8%A1%8C%E5%91%BD%E4%BB%A4%E6%9E%84%E5%BB%BAchatgpt%20webapp,%E6%94%AF%E6%8C%81%E9%AB%98%E5%B9%B6%E5%8F%91%E4%BB%A5%E5%8F%8A%E4%B8%8A%E4%B8%8B%E6%96%87%E5%AF%B9%E8%AF%9D%E5%8A%9F%E8%83%BD/" title="三分钟4行命令构建chatgpt webapp,支持高并发以及上下文对话功能"><img src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/image-20230305093941469.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="三分钟4行命令构建chatgpt webapp,支持高并发以及上下文对话功能"/></a><div class="content"><a class="title" href="/2023/03/04/%E4%B8%89%E5%88%86%E9%92%9F4%E8%A1%8C%E5%91%BD%E4%BB%A4%E6%9E%84%E5%BB%BAchatgpt%20webapp,%E6%94%AF%E6%8C%81%E9%AB%98%E5%B9%B6%E5%8F%91%E4%BB%A5%E5%8F%8A%E4%B8%8A%E4%B8%8B%E6%96%87%E5%AF%B9%E8%AF%9D%E5%8A%9F%E8%83%BD/" title="三分钟4行命令构建chatgpt webapp,支持高并发以及上下文对话功能">三分钟4行命令构建chatgpt webapp,支持高并发以及上下文对话功能</a><time datetime="2023-03-03T16:00:00.000Z" title="发表于 2023-03-04 00:00:00">2023-03-04</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2023/03/03/%E5%A4%9A%E6%A0%87%E7%AD%BE%E5%88%86%E7%B1%BB%E7%9A%84CrossEntropyLoss%E5%88%B0%E5%BA%95%E9%9C%80%E4%B8%8D%E9%9C%80%E8%A6%81One-Hot%E7%BC%96%E7%A0%81/" title="多标签分类的CrossEntropyLoss到底需不需要One-Hot编码"><img src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/image-20230303211538791.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="多标签分类的CrossEntropyLoss到底需不需要One-Hot编码"/></a><div class="content"><a class="title" href="/2023/03/03/%E5%A4%9A%E6%A0%87%E7%AD%BE%E5%88%86%E7%B1%BB%E7%9A%84CrossEntropyLoss%E5%88%B0%E5%BA%95%E9%9C%80%E4%B8%8D%E9%9C%80%E8%A6%81One-Hot%E7%BC%96%E7%A0%81/" title="多标签分类的CrossEntropyLoss到底需不需要One-Hot编码">多标签分类的CrossEntropyLoss到底需不需要One-Hot编码</a><time datetime="2023-03-02T16:00:00.000Z" title="发表于 2023-03-03 00:00:00">2023-03-03</time></div></div><div class="aside-list-item"><a class="thumbnail" href="/2023/02/28/GPTEX---%E4%B8%BAChatGPT%E8%80%8C%E7%94%9F%E7%9A%84latex%E8%BD%AF%E4%BB%B6/" title="GPTEX---为ChatGPT而生的latex软件"><img src="https://nmhjklnm.oss-cn-beijing.aliyuncs.com/article-img/img/image-20230228145723183.png" onerror="this.onerror=null;this.src='/img/404.jpg'" alt="GPTEX---为ChatGPT而生的latex软件"/></a><div class="content"><a class="title" href="/2023/02/28/GPTEX---%E4%B8%BAChatGPT%E8%80%8C%E7%94%9F%E7%9A%84latex%E8%BD%AF%E4%BB%B6/" title="GPTEX---为ChatGPT而生的latex软件">GPTEX---为ChatGPT而生的latex软件</a><time datetime="2023-02-27T16:00:00.000Z" title="发表于 2023-02-28 00:00:00">2023-02-28</time></div></div></div></div></div></div></main><footer id="footer"><div id="footer-wrap"><div class="copyright">&copy;2022 - 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